CFGP: Closed-Form Graph Prompting without Per-Task Training

Few-shot graph learning typically proceeds by pre-training a graph neural network (GNN) on unlabeled graph data and adapting it to a labeled downstream task. Graph prompt learning freezes this encoder and adapts a small set of prompts instead of fine-tuning all parameters. Existing methods, however, still typically train prompt parameters for each task, which is costly and sensitive to initialization, learning rate, and early stopping under extreme label scarcity. We propose CFGP (Closed-Form Graph Prompting without Per-Task Training), which builds an instance-specific input prompt in closed form from class signatures derived from the labeled examples and a clustering of all instances, then reads it out with the frozen GNN and a prototype classifier. Adaptation uses no optimizer and no training objective. The same construction covers node- and graph-level tasks, differing only in how the prompt is injected, and cuts adaptation time by more than an order of magnitude at competitive accuracy.